Download Source Package shogun:
SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing.
SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the Octave package.
|
|
|
| Architecture | Package Size | Installed Size | Files |
|---|---|---|---|
| alpha | 122.3 kB | 1032 kB | [list of files] |
| amd64 | 120.6 kB | 996 kB | [list of files] |
| armel | 117.1 kB | 980 kB | [list of files] |
| hppa | 122.2 kB | 1008 kB | [list of files] |
| i386 | 114.9 kB | 980 kB | [list of files] |
| ia64 | 130.4 kB | 1096 kB | [list of files] |
| mips | 113.3 kB | 1012 kB | [list of files] |
| mipsel | 110.8 kB | 1012 kB | [list of files] |
| powerpc | 120.3 kB | 1008 kB | [list of files] |
| s390 | 116.0 kB | 996 kB | [list of files] |
| sparc | 113.6 kB | 984 kB | [list of files] |